The present teachings relate generally to method and apparatus for generating user profiles based on periodic location fixes, and more particularly to platforms and techniques for automatically generating user demographic or marketing profiles based on user location history tracked via a mobile telephone or other location-aware device.
Advertising content and other media can be targeted, delivered, and priced as a function of the demographic profile of the intended recipients. For example, broadcast television media can contain advertising content that is tailored to the collective demographic profile of a given geographic region, such as a county or metropolitan area. The aggregate demographic profile of a certain city can, for instance, consist of an average age of 40 to 50 years, possibly with a most-likely gender or average household size, and within certain income bands. Advertisers or other content providers can therefore direct products and services of interest to the target population more effectively, and media such as broadcast or cable television stations can price and schedule advertising slots more appropriately for a defined demographic region. However, even when effectively employed, metro-scale marketing studies must often be generated through manual efforts, such as surveys, census reports, or other kinds of information gathering which do not lend themselves to automatic data collection or timely updating.
According to other content delivery platforms in the Internet space, a user's history of visited Web sites, purchase, or content selection choices can be tracked to generate a predicted profile of sites or content of interest to that single user. The delivery of media based on profiles developed on aggregate geographic scales or Web usage involves, however, a number of drawbacks. Demographic profiling at the relatively coarse level of county or metropolitan populations, for instance, necessarily overlooks potential sub-populations within those relatively large geographic areas that could represent potentially valuable target communities. Certain marketing platforms such as Claritas Prizm™ NE can provide household-level segmentation, but even this segmentation code is then assumed for every person in the household irrespective of their age group or financial responsibilities. The potential for building targeted marketing campaigns or other media directed at a more granular level is therefore not realized.
Web usage patterns in their own turn can provide a predictor of future Web behavior for individual users and possibly, groups of users, but may not predict behavior outside that environment, including mobility and consumption patterns around a user's home location. Consumers may not receive the benefit of customized product and service offerings that identifies small or concentrated communities, households, or individuals within households, due to the lack of resolution in consumer profiling platforms, as they exist today.